code: def _squeeze_dims_fx(
      tensor: torch.Tensor, tensor_dim: int
  ) -> torch.Tensor:
      if tensor_dim == 4:
          pass
      elif tensor_dim == 3:
          tensor = tensor.squeeze(1)
      else:
          raise NotImplementedError(
              f"Unsupported input dimension {tensor.shape}"
          )
      return tensor
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  import torch.fx
  import torch.nn
  from torch.nn.common_types import _size_3_t
  from .drop_path import DropPath
label_desc: Use image node_id 3 for calling a global function with example usage: _squeeze_dims_fx(tensor, tensor_dim) and returns: tensor